Related papers: Stellar Populations With Optical Spectra: Deep Lea…
Stellar population measurements in integral field unit surveys are often limited by low signal-to-noise ratios (S/N) in low-surface-brightness spaxels. Using controlled synthetic experiments, we test whether deep-learning-based denoising…
We aim to develop a model-driven deep learning approach to age determination, by training neural networks on stellar evolutionary grids. Contrary to the usual data-driven deep learning approach of using prior age estimates as training data,…
Stellar population properties are crucial for understanding galaxy evolution. Their inference for statistically representative samples requires deep multi-object spectroscopy, typically obtained with fiber-fed spectrographs that integrate…
Accurate stellar population synthesis models are vital in understanding the properties and formation histories of galaxies. In order to calibrate and test the reliability of these models, they are often compared with observations of star…
In this work, we investigate the reliability of spectral synthesis methods in the estimation of the mean stellar age and metallicity, addressing the question of which signal-to-noise ratios (S/N) are needed to determine these quantities. To…
Over the last decades, evolutionary population synthesis models have powered an unmatched leap forward in our understanding of galaxies. From dating the age of the first galaxies in the Universe to detailed measurements of the chemical…
Upcoming large-area narrow band photometric surveys, such as J-PAS, will enable us to observe a large number of galaxies simultaneously and efficiently. However, it will be challenging to analyse the spatially-resolved stellar populations…
Determining the properties of old stellar populations (those with age >1 Gyr) has long involved the comparison of their integrated light, either in the form of photometry or spectroscopic indexes, with empirical or synthetic templates. Here…
We introduce a practical methodology for investigating the star formation and chemical evolution history of a galaxy: age-divided mean stellar populations (ADPs) from full spectrum fitting. In this method, the mass-weighted mean stellar…
The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain…
The field of galaxy evolution will make a great leap forward in the next decade as a consequence of the huge effort by the scientific community in multi-object spectroscopic facilities. To maximise the impact of such incoming data, the…
The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph. This new instrument will be exploited to obtain high S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE Stellar…
Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…
In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest…
Stellar population studies of early-type galaxies (ETGs) based on their optical stellar continuum suggest that these are quiescent systems. However, emission lines and ultraviolet photometry reveal a diverse population. We use a new version…
It is well known that, when analyzed at the light of current synthesis model predictions, variations in the physical properties of single stellar populations (e.g. age, metallicity, initial mass function, element abundance ratios) may have…
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…
Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of…
In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we compare the stellar parameters determined using five popular…
We test how well available stellar population models can reproduce observed u,g,r,i,z-band photometry of the local galaxy population (0.02<=z<=0.03) as probed by the SDSS. Our study is conducted from the perspective of a user of the models,…